README.md

Code of paper "Learning to Parse Wireframes in Images of Man-Made Environments", CVPR 2018

Folder/file	Description
junc	For training junction detector.
linepx	For training straight line pixel detector.
wireframe.py	Generate line segments/wireframe from predicted junctions and line pixels.
evaluation	Evaluation of junctions and wireframes.

Requirements

• python3
• pytorch==0.3.1
• opencv==3.3.1
• scipy, numpy, progress, protobuf
• joblib (for parallel processing data.)
• tqdm
• [optional] dominate

The code is written and tested in python3, please install all requirements in python3.

Prepare data

• Download the training data.
  • Download imgs from OneDrive, put it in data/, unzip v1.1.zip.
  • Download annotation from OneDrive, put it in data/, unzip pointlines.zip.
• Preprocess data.

  cd junc
  python3 main.py --create_dataset --exp 1 --json
  
  cd linepx
  python3 main.py --genLine
  

Note: --json means you put the hype-parameters in junc/hypes/1.json.

Training

• train junction detector.

  cd junc
  python3 main.py --exp 1 --json --gpu 0 --balance
  

• train line pixel detecor.

  cd linepx
  python3 main.py --netType stackedHGB --GPUs 0 --LR 0.001 --batchSize 4
  

Testing

• Test junction detector.

  cd junc
  python3 main.py --exp 1 --json --test --checkepoch 16 --gpu 0 --balance
  

• Test line pixel detector.

  cd linepx
  python3 main.py --netType stackedHGB --GPUs 0 --LR 0.001 --testOnly t
  

• Combine junction and line pixel prediction.

  python wireframe.py
  

Evaluation

The code for evaluation is put in evaluation/junc and evaluation/wireframe. Expected precision/recall curve is like and .

Visualize the result

For visualizing the result, we recommend generating an html file using dominate to visualize the result of different methods in columns.

Citation

@InProceedings{wireframe_cvpr18,
author = {Kun Huang and Yifan Wang and Zihan Zhou and Tianjiao Ding and Shenghua Gao and Yi Ma},
title = {Learning to Parse Wireframes in Images of Man-Made Environments},
booktitle = {CVPR},
month = {June},
year = {2018}
}

License

You can use this code for your research and other usages, following BSD 2-Clause license. please credit our work when it helps you.